Improving Wireless Simulation Through Noise Modeling

We propose modeling environmental noise in order to efficiently and accurately simulate wireless packet delivery. We measure noise traces in many different environments and propose three algorithms to simulate noise from these traces. We evaluate applying these algorithms to signal-to-noise curves in comparison to existing simulation approaches used in EmStar, TOSSIM, and ns2. We measure simulation accuracy using the Kantorovich-Wasserstein distance on conditional packet delivery functions. We demonstrate that using a closest-fit pattern matching (CPM) noise model can capture complex temporal dynamics which existing approaches do not, increasing packet simulation fidelity by a factor of 2 for good links, a factor of 1.5 for bad links, and a factor of 5 for intermediate links. As our models are derived from real-world traces, they can be generated for many different environments.

[1]  Jens Palsberg,et al.  Avrora: scalable sensor network simulation with precise timing , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[2]  Deborah Estrin,et al.  Temporal Properties of Low Power Wireless Links: Modeling and Implications on Multi-Hop Routing , 2005 .

[3]  Deborah Estrin,et al.  A system for simulation, emulation, and deployment of heterogeneous sensor networks , 2004, SenSys '04.

[4]  C. Givens,et al.  A class of Wasserstein metrics for probability distributions. , 1984 .

[5]  Bhaskar Krishnamachari,et al.  Experimental study of concurrent transmission in wireless sensor networks , 2006, SenSys '06.

[6]  S. Seidel,et al.  914 MHz path loss prediction models for indoor wireless communications in multifloored buildings , 1992 .

[7]  Deborah Estrin,et al.  GHT: a geographic hash table for data-centric storage , 2002, WSNA '02.

[8]  Marco Zuniga,et al.  Analyzing the transitional region in low power wireless links , 2004, 2004 First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004..

[9]  Norman M. Abramson,et al.  Nonlinear transformations of random processes , 1967, IEEE Trans. Inf. Theory.

[10]  H. Nyquist.  Thermal Agitation of Electric Charge in Conductors , 1928 .

[11]  Philip Levis,et al.  Understanding the causes of packet delivery success and failure in dense wireless sensor networks , 2006, SenSys '06.

[12]  D. A. Conner,et al.  Modelling of stochastic system inputs having prescribed distribution and covariance functions , 1979 .

[13]  A. Kareem,et al.  Equivalent Statistical Cubicization for System and Forcing Nonlinearities , 1997 .

[14]  J. Johnson.  Thermal Agitation of Electricity in Conductors , 1928 .

[15]  David E. Culler,et al.  TOSSIM: accurate and scalable simulation of entire TinyOS applications , 2003, SenSys '03.

[16]  Leonidas J. Guibas,et al.  A metric for distributions with applications to image databases , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[17]  Deborah Estrin,et al.  An Empirical Study of Epidemic Algorithms in Large Scale Multihop Wireless Networks , 2002 .

[18]  Deborah Estrin,et al.  SCALE: A tool for Simple Connectivity Assessment in Lossy Environments , 2003 .

[19]  Deborah Estrin,et al.  Complex Behavior at Scale: An Experimental Study of Low-Power Wireless Sensor Networks , 2002 .

[20]  H. Hashemi,et al.  The indoor radio propagation channel , 1993, Proc. IEEE.

[21]  Ramesh Govindan,et al.  Understanding packet delivery performance in dense wireless sensor networks , 2003, SenSys '03.

[22]  J. Johnson.  Thermal Agitation of Electricity in Conductors , 1927, Nature.

[23]  G. E. Johnson.  Constructions of particular random processes , 1994, Proc. IEEE.